Faktion AI Health Check Survey

Question Title

* 1. How is data treated in your company?

No vision on the value of data. Data monetization happens only when individuals push for it.

Data monetization happens on an ad-hoc basis. Stakeholders understand the value of their data.

Data is recognized as valuable and treated as such. Monetization is a priority for stakeholders.

Established revenue streams of monetized data. Data is consistently used as a strategic and competitive advantage. Data partnerships are in place with other companies.

Dedicated monetization team that actively hunts for opportunities and educates others about the inherent value of their data.

Question Title

* 2. How are decisions made in your company?

Company relies on 'gut feel' or 'best practices'. Decision are made without data.

Evidence-based decision making. Data is used as objective input for decisions. Before making most operational decisions, objective criteria are established

Daily operations are managed by data driven decisions. A full view of the company is available in dashboards that guide the day-to-day operations.

Machine Learning is trusted for daily operational decision making. The return on these decisions is monitored closely and the decision criteria or models updated and improved continuously.

Employees are educated on a regular basis on the potential and possibilities of AI decision making. Human in the loop decision making.

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* 3. How well is AI integrated and in which parts of the organization?

No roadmap defined. Scattered vision and projects.

Company or team-wide assets exist that prioritize roadmap and projects.

Clear strategy that has been communicated to the whole team. Target goals and priorities have been set and communicated.

AI strategy is executed on a regular basis. Fixed targets and goals in place to assess performance and execution.

Regular process reviews on reviewing AI strategy and projects. Roadmap gets revised and prioritized. People are accountable.

Question Title

* 4. How is data being collected in your organization?

Unclear data collection or retention strategy. "Collect everything". Individual efforts to collect extra data.

Data is collected consistently and which a purpose in mind. Data collection guidelines exist and are used.

Defined data collection strategy based on potential value and uniqueness of data sources. Strategic priority on increasing the potential of data by enriching it, gathering extra data.

Regular feedback loops on collecting data. Regular reviews on the way data is collected and stored. Data collection is built-in step for all new projects or initiatives.

Data treated as a competitive advantage. Data collection and maintenance is a core strategic initiative. Value of collected data is part of the business case when introducing new projects.

Question Title

* 5. What governance is in place to ensure data quality?

No view on data quality or process to manage and improve it. No standardization of terminology, identifiers, codes, …

Investments are made to improve and clean data. Data quality is enforced at the source.

Data is stored in a standardized way with clear identifiers, mappings and centralized data dictionaries. Data quality and standardization guidelines exist and are enforced.

Process for detecting dark data sources and working on actively incorporating all data sources. Golden records, master records and master data management exist that reconcile versions.

Full view on dependencies between data sources using a data governance framework. Quality indicators on data quality. Continuous improvement process of data quality starting at data collection up to the use of the data.

Question Title

* 6. How accessible is data throughout your company?

Scattered data sources. Multiple systems. Unconnected systems or dark data. (Excel, …).

Data is stored in multiple data warehouse(s) and databases. Documentation exists of what kind of data, data formats. Access request manual or only IT has access

Centralized data warehouse. Standard tools are available. Data dictionaries are available. Data is accessible and usable on request by non-IT.

Clear, auditable and systemic data access request. Data marts are easily created on request to expose data to as many people as possible. Defined process to get from exploration to deployment

Defined but flexible reference architecture. 360-view granular data that combines all. Exploration tools and training to use them to actively use data.

Question Title

* 7. What is the current maturity and awareness level of your people?

Few data savvy resources. No or very few deployed ML models

People understand what is possible. Team has experience with POC's. Some models have been deployed.

Awareness of technological possibilities at every level. Industry specific knowledge of data techniques. Team knows what is possible and can execute on projects.

Managers are aware of potential and how to spot good opportunities for AI and analytics. AI Team has deep expertise in project methodology and executing AI projects efficiently from idea to deployment.

Niche experts that combine industry insight with AI expertise. AI delivery methodology is reviewed regularly.

Question Title

* 8. What training and development plans are in place for your people to become more data-driven?

Ad-hoc training request. No vision on development or career paths. Training as individual responsibility.

Standardized courses are available. Knowledge is shared in an informal way. Training as shared responsibility in team.

Data-relevant career paths exist and are treated as valuable as management career paths. Repositories with training materials and documentation exist for different competencies.

Frequent knowledge sharing sessions for applications, techniques and tools. Skills matrices are filled in and used to prioritize training based on coverage.

Training and career paths are audited, quality controlled and continuously improved.

Question Title

* 9. How well is your organization adapted to become an AI-enabled company?

Competencies do not exist, are unclear or are scattered around the company. No or only ad hoc knowledge sharing

Leaders in their field have been identified. Knowledge is shared 1-1 and in sharing sessions. No defined process for deployment. No defined process for managing model life-cycle.

Coordinated sharing of resources. Clear and open sharing of competency-skill matrix per team member. Regular knowledge sharing sessions. Ad hoc treatment of model life-cycle.

Career framework for technical and data savvy people. Data experts or data savvy people in every team. Defined model life-cycle process.

Center of excellence, competency centers, internal consulting services. Clear and auditable process of model life-cycle with assigned responsibilities at every step.

Question Title

* 10. How does your organization handle data sensitivity and privacy?

Data is not classified, nor has any sensitivity labels.

A data classification policy is introduced, yet not enforced through technical controls.

Centralized data governance structure. Audit logs on all data access.

Mandatory compliance assessment for every project. Privacy is treated as a competitive differentiator instead of a liability

DPO (Data Protection Officer) for every project. Data protection and privacy controls are part of the non-functional requirements of any project.

Question Title

* 11. Which technical security standards are in place to protect data?

Responsibility lies at the individual or in IT. Broken or over-engineered processes.

Education on responsibilities. Defined security sensitivity classes.

Regular security audits.

Regular security audits and mandatory risk assessment for all projects that use data.

Enforced but fast security protocols. Regular penetration testing.

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* 12. How well has GDPR regulation been implemented so far in your organization?

Stakeholders are aware of the GDPR regulations, but no measures have been introduced yet, or at most a readiness assessment is ongoing.

Privacy and Data Protection regulatory compliance is being executed and ongoing. No formalized frameworks are enforced yet.

Privacy Impact Assessments (PIA) are conducted for system, data and application changes, and are part of the overall Change and Release Management processes

Mandatory compliance assessment for every project

DPO (Data Protection Officer) for every project. Data protection and privacy controls are part of the non-functional requirements of any project.

Personal information

Your e-mail address will only be used to send you the results soon.

Question Title

* 13. What's your e-mail address?

Question Title

* 14. Let's get in touch!

Please let us know what you want to discuss:

I want to register for the StartAI workshop.

I want more information about the workshop.

I want to discuss the results of this healthcheck.

Nothing, thank you.

Faktion AI Health Check

Question Title

* 1. How is data treated in your company?

Question Title

* 2. How are decisions made in your company?

Question Title

* 3. How well is AI integrated and in which parts of the organization?

Question Title

* 4. How is data being collected in your organization?

Question Title

* 5. What governance is in place to ensure data quality?

Question Title

* 6. How accessible is data throughout your company?

Question Title

* 7. What is the current maturity and awareness level of your people?

Question Title

* 8. What training and development plans are in place for your people to become more data-driven?

Question Title

* 9. How well is your organization adapted to become an AI-enabled company?

Question Title

* 10. How does your organization handle data sensitivity and privacy?

Question Title

* 11. Which technical security standards are in place to protect data?

Question Title

* 12. How well has GDPR regulation been implemented so far in your organization?

Question Title

* 13. What's your e-mail address?

Question Title

* 14. Let's get in touch!

Please let us know what you want to discuss:

Question Title

* 15. What's your company name?

Question Title

* 16. On which mobile number may we contact you?

Question Title

* 17. GDPR

Faktion AI Health Check

Question Title

* 1. How is data treated in your company?

Question Title

* 2. How are decisions made in your company?

Question Title

* 3. How well is AI integrated and in which parts of the organization?

Question Title

* 4. How is data being collected in your organization?

Question Title

* 5. What governance is in place to ensure data quality?

Question Title

* 6. How accessible is data throughout your company?

Question Title

* 7. What is the current maturity and awareness level of your people?

Question Title

* 8. What training and development plans are in place for your people to become more data-driven?

Question Title

* 9. How well is your organization adapted to become an AI-enabled company?

Question Title

* 10. How does your organization handle data sensitivity and privacy?

Question Title

* 11. Which technical security standards are in place to protect data?

Question Title

* 12. How well has GDPR regulation been implemented so far in your organization?

Question Title

* 13. What's your e-mail address?

Question Title

* 14. Let's get in touch! Please let us know what you want to discuss:

Question Title

* 15. What's your company name?

Question Title

* 16. On which mobile number may we contact you?

Question Title

* 17. GDPR

* 14. Let's get in touch!

Please let us know what you want to discuss: